Studies in Nonlinear Dynamics & Econometrics

Published by De Gruyter

Volume 16 Issue 5

December 2012

Issue of Studies in Nonlinear Dynamics & Econometrics

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December 12, 2012

Threshold Asymmetries in Equity Return Distributions: Statistical Tests and Investment Implications

Emre Yoldas

Abstract

In this paper we investigate asymmetries in time-varying means, volatilities, correlations, and betas of equity returns in a multivariate threshold framework. We consider alternative specifications in which the threshold variable is based on well established equity pricing factors and predictors. We find strong threshold effects with respect to market excess return, value premium, and term spread. Our results indicate that the threshold model based on the market excess return provides a flexible and computationally inexpensive specification for modeling asymmetries. We test significance of specific forms of asymmetries using subsampling methods. We compare performance of the proposed threshold model with a variety of alternatives in an out-of-sample setup and find that the threshold model performs remarkably well, especially for investors with relatively high risk aversion.

December 12, 2012

Estimation of a Nonlinear Taylor Rule Using Real-Time U.S. Data

Jean-Francois Lamarche, Zisimos Koustasy

Abstract

This paper extends the work in Orphanides (2003) by re-examining the empirical evidence for a Taylor rule in a nonlinear framework. In doing so, it updates the Greenbook dataset used by the afore mentioned author to the most recent available period. A three-regime threshold regression model is utilized to capture the possibly asymmetric policy reaction function used by the U.S. Federal Reserve. The theoretical foundations for such an approach to monetary policy are discussed in Orphanides and Wilcox (2002). Our results indicate that the estimated Taylor rule for the U.S., based on real-time Greenbook data for the period 1982:3-2003:4, is probably nonlinear.

December 12, 2012

Predicting Stock Returns Using a Variable Order Markov Tree Model

Armin Shmilovici, Irad Ben-Gal

Abstract

The weak form of the Efficient Market Hypothesis (EMH) states that the current market price fully reflects the information of past prices and rules out predictions based on price data alone. In an efficient market, consistent prediction of the next outcome of a financial time series is problematic because there are no reoccurring patterns that can be used for a reliable prediction. This research offers an alternative test of the weak form of the EMH. It uses a universal prediction algorithm based on the Variable Order Markov tree model to identify re-occurring patterns in the data, constructs explanatory models, and predicts the next time-series outcome. Based on these predictions, it rejects the EMH for certain stock markets while accepting it for other markets. The weak form of the EMH is tested for four international stock exchanges: the German DAX index; the American Dow-Jones30 index; the Austrian ATX index and the Danish KFX index. The universal prediction algorithm is used with sliding windows of 50, 75, and 100 consecutive daily returns for periods of up to 12 trading years. Statistically significant predictions are detected for 17% to 81% of the ATX, KFX and DJ30 stock series for about 3% to 30% of the trading days. A summary prediction analysis indicates that for a confidence level of 99% the more volatile German (DAX) and American (DJ30) markets are indeed efficient. The algorithm detects periods of potential market inefficiency in the ATX and KFX markets that may be exploited for obtaining excess returns.

December 12, 2012

How Do You Make A Time Series Sing Like a Choir? Extracting Embedded Frequencies from Economic and Financial Time Series using Empirical Mode Decomposition

Patrick M. Crowley

Abstract

Empirical Mode Decomposition (EMD) was developed late last century, but has still to be introduced to the vast majority of economists. EMD was originally one of the components of Hilbert Huang Transform (HHT) which was a process of extracting the frequency mode features of cycles embedded in any time series using an adaptive data method which can be applied without making any assumption about stationarity or linear data-generating properties of time series. This paper introduces economists to the two constituent parts of the HHT transform, namely EMD and the Hilbert Spectrum, and also a new variant of this methodology, Ensemble EMD (EEMD). Several illustrative applications using the methodology are also included.

December 12, 2012

The Transitional Dynamics of an Endogenous Growth Model: Generalizing Production Functions

Manuel A. Gómez, Tiago Neves Sequeira

Abstract

This paper devises a class of endogenous growth models with physical capital, human capital and product varieties. Unlike previous literature, we introduce a general specification of production that allows for disentangling the gains from specialization from the monopolistic markup. The consequences of separating both parameters on the equilibrium dynamics of the model are analyzed. We find that the long-run growth rate ceases to depend on the markup when it is disentangled from the returns to specialization, and the same happens with the conditions for saddle-path stability. Numerical simulations show that the steady-state equilibria are more prone to be stable in the model that disentangles parameters. Furthermore, the model is able to generate a rich variety of development scenarios, accounting for a number of stylized facts related to the development process of industrialized countries.

December 12, 2012

Unit Root Testing with Stationary Covariates in the Framework of Asymmetric STAR Nonlinearity

Ching-Chuan Tsong

Abstract

This paper generalizes the univariate unit root test proposed by Sollis (2009) by adding correlated covariates for a power boost. The asymptotic distribution of the proposed test is derived, and the asymptotic critical values are tabulated. Simulation experiments are conducted to demonstrate that for both small and moderate sample sizes found in applications, the new test can exhibit an excellent control over the empirical sizes, and most importantly, it delivers larger power gains from inclusion of correlated covariates, as compared to the counterpart without covariates. An application to real exchange rates for 18 Asian countries suggests that our proposed test is able to unveil stronger evidence in favour of mean reversion in real exchange rates than the counterpart without covariates does. Moreover, several real exchange rates are tested to display asymmetries in the adjustment process towards their equilibrium values.

About this journal

Objective
A peer-reviewed journal since 1996, Studies in Nonlinear Dynamics & Econometrics (SNDE) is at the forefront of statistical and theoretical approaches to economics. The journal studies ways in which econometrics and dynamical systems theory increase our understanding of economic and financial markets. The journal disseminates authors' algorithms, programs, and data sets, allowing other scholars to replicate empirical results. Authors include econometricians such as Clive Granger, James Hamilton, and Halbert White, and theorists Jess Benhabib, Alan Kirman, and Kazuo Nishimura.

Best Paper Award
Since 2015, the Society has awarded $500 each year to the best paper published in the Society’s journal: Studies in Nonlinear Dynamics and Econometrics.

The Best Paper in 2023 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Thomas Lux for his paper: "Approximate Bayesian Inference for Agent-Based Models in Economics: A Case Study." Studies in Nonlinear Dynamics & Econometrics, vol. 27, no. 4, 2023, pp. 423-447.

The Best Paper in 2022 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Szabolcs Blazsek, Alvaro Escribano and Adrian Licht. for their paper: "Multivariate Markov-switching score-driven models: an application to the global crude oil market" Studies in Nonlinear Dynamics & Econometrics, vol. 26, no. 3, 2022, pp. 313-335.

The Best Paper in 2021 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Eric Mark Aldrich and Howard Kung for their paper: “Computational Methods for Production-Based Asset Pricing Models with Recursive Utility”, published in Volume 25, Issue 1.

The Best Paper in 2020 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Gregor Bäurle, Daniel Kaufmann, Sylvia Kaufmann and Rodney Strachan for their paper: “Constrained interest rates and changing dynamics at the zero lower bound”, published in Volume 24, Issue 2.

The Best Paper in 2019 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Chang-Jin Kim and Yunmi Kim for their paper: “A unified framework jointly explaining business conditions, stock returns, volatility and “volatility feedback news” effects”, published in Volume 23, Issue 2.

The Best Paper in 2018 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Luiggi Donayre, Yunjong Eo and James Morley for their paper: “Improving likelihood-ratio-based confidence intervals for threshold parameters in finite samples”,published in Volume 22, Issue 1 (Feb 2018)

The Best Paper in 2017 for Studies in Nonlinear Dynamics and Econometrics has been awarded to Julien Chevallier and Stephane Goutte for their paper: “On the estimation of regime-switching Levy models”, published in Volume 21, Issue 1 (Feb 2017).

The Best Paper in 2016 for Studies in Nonlinear Dynamics and Econometrics has been awarded to Mark J. Jensen for his paper: “Robust estimation of nonstationary, fractionally integrated, autoregressive, stochastic volatility?”, published in Volume 20, Issue 4 (Sept. 2016).

The Best Paper in 2015 for Studies in Nonlinear Dynamics and Econometrics has been awarded to Markus Jochmann and Gary Koop for their paper: “Regime-switching cointegration?”, published in Volume 19, Issue 1 (Feb 2015).

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Macroeconomics
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